Mahbub et al. Environmental Health and Preventive Medicine (2017) 22:7 DOI 10.1186/s12199-017-0609-8
REGULAR ARTICLE
Environmental Health and Preventive Medicine Open Access
Alteration in plasma free amino acid levels and its association with gout MH Mahbub1, Natsu Yamaguchi1, Hidekazu Takahashi1, Ryosuke Hase1, Hiroki Amano2, Mikiko Kobayashi-Miura3, Hideyuki Kanda4, Yasuyuki Fujita4, Hiroshi Yamamoto5, Mai Yamamoto5, Shinya Kikuchi5, Atsuko Ikeda5, Naoko Kageyama5, Mina Nakamura5, Yasutaka Ishimaru1, Hiroshi Sunagawa1 and Tsuyoshi Tanabe1*
Abstract Background: Studies on the association of plasma-free amino acids with gout are very limited and produced conflicting results. Therefore, we sought to explore and characterize the plasma-free amino acid (PFAA) profile in patients with gout and evaluate its association with the latter. Methods: Data from a total of 819 subjects (including 34 patients with gout) undergoing an annual health examination program in Shimane, Japan were considered for this study. Venous blood samples were collected from the subjects and concentrations of 19 plasma amino acids were determined by high-performance liquid chromatography–electrospray ionization–mass spectrometry. Student’s t-test was applied for comparison of variables between patient and control groups. The relationships between the presence or absence of gout and individual amino acids were investigated by logistic regression analysis controlling for the effects of potential demographic confounders. Results: Among 19 amino acids, the levels of 10 amino acids (alanine, glycine, isoleucine, leucine, methionine, phenylalanine, proline, serine, tryptophan, valine) differed significantly (P < .001 to .05) between the patient and control groups. Univariate logistic regression analysis revealed that plasma levels of alanine, isoleucine, leucine, phenylalanine, tryptophan and valine had significant positive associations (P < .005 to .05) whereas glycine and serine had significant inverse association (P < .05) with gout. Conclusions: The observed significant changes in PFAA profiles may have important implications for improving our understanding of pathophysiology, diagnosis and prevention of gout. The findings of this study need further confirmation in future large-scale studies involving a larger number of patients with gout. Keywords: Amino acids, Plasma, Profile, Gout, Relationship
Background Gout, the most prevalent inflammatory joint disease is usually characterized by recurrent attacks of intense pain. It is predominant in men and also in older women [1]. Currently, the global prevalence of gout is estimated to be at least 1–2% in the general population and 3–4% in the adult population [2]. The prevalence can be as high as 7% among the male population over 75 years of age [3]. In Japan, the overall prevalence of gout was * Correspondence:
[email protected] 1 Department of Public Health and Preventive Medicine, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan Full list of author information is available at the end of the article
found to be lower in a survey which was 0.51% overall and 1.1% amongst men [4]. The incidence and prevalence of gout are found to be increasing in many parts of the world [5, 6]. With the aging of world population, the global burden of gout continues to rise [7]. Therefore, better understanding of various factors and mechanisms underlying the pathophysiology of gout is necessary for the proper diagnosis and management of it. The most important underlying mechanism in gout involves elevated levels of uric acid in the blood. Such a persistent increase in the levels of serum uric acid causes crystallization of it and intra-articular formation and deposition of monosodium urate (MSU) crystals which can
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Mahbub et al. Environmental Health and Preventive Medicine (2017) 22:7
trigger the painful attacks of gout [8]. The serum uric acid is the end product of an exogenous (from food) pool of purines and endogenous (from liver, intestines, muscles, kidneys and the vascular endothelium) purine metabolism [9]. Certain amino acids take part in the biosynthesis of purine and subsequent formation of uric acids. For example, amino acids like glutamine, glycine, and serine are utilized in increased amounts for the formation of uric acid in gout [10]. Therefore, it is reasonable to postulate that amino acids play important roles in the pathogenesis of gout. Plasma free amino acids (PFAAs) abundantly circulate as a medium linking all organ systems in the human body; the PFAA profiles have been shown to be influenced by metabolic variations in specific organ systems induced by specific diseases [11]. PFAA profiles can be used as reliable markers for monitoring the risks of various diseases in various populations and also the improvements in physiological states [12–14]. Therefore, amino acid profiles are increasingly being used in the evaluation of various diseases. Analysis of PFAA profiles with a high degree of validity and reliability can be useful in understanding the underlying pathophysiology and assessing the severity of a disorder. Furthermore, various indexes developed from PFAA profiles have shown the potential for diagnosis of various pathological conditions [11, 14, 15]. Understanding the variations in PFAA profiles among various populations including patients with gout seems to be important which may guide to the diagnosis of gout. However, the number of studies investigating the changes in PFAA profiles associated with gout is very limited. Also, there is a lack of published research works, especially in recent times, on the relationship between the amino acid profiles and gout. A few previous studies studies reported altered PFAA profiles in patients with gout. Compared to control subjects, some researchers reported a significant increase for some of the amino acids and a significant decrease for the others in patients with gout [10, 16], whereas other researchers reported hyperaminoacidemia for all the investigated amino acids in the patients or similar amino acid spectrums in normal subjects and patients with gout [17, 18]. As understandable, the findings are conflicting as the observed changes in those studies are inconsistent for various amino acids. Furthermore, those studies aimed at finding the significant group differences between gout patients and control subjects in the concentrations of various amino acids, and did not investigate the relationship of amino acids with gout. Therefore, the purpose of the present study was to further explore and characterize the PFAA profiles in patients with gout and evaluate its association with the latter.
Methods A total of 831 subjects who underwent their annual health check-up between June and July 2012 at different
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health examination centers in Shimane Prefecture, Japan and for whom workplace health examination was not applicable, were considered for inclusion in this study. The health examination included physical examination, clinical and laboratory tests, and a self-administered questionnaire containing personal and medical history. Based on the questionnaire data, 12 subjects were excluded from the study due to lack of information on gout. Finally, 34 subjects were identified as having an established diagnosis of gout and were treated as the patient population, and the rest 785 subjects were treated as the control population in this study. Among the patients, 26 were currently taking medications for gout and the rest 8, currently without any medications for it. The subjects had no serious health problems like cancer or renal failure. An oral explanation of the study protocol was made in detail to the study participants and written informed consent was obtained from all of them. The current study protocol was approved by the institutional review board of Shimane University Hospital (No. H25-26-2). Measurement of plasma amino acid concentrations
Five ml of blood samples were collected and analyzed for plasma amino acid concentrations following the protocol previously described elsewhere [11, 19–21]. Briefly, after overnight fasting, venous blood samples were collected from the cubital vein of the seated subjects in tubes which contained ethylenediaminetetraacetic acid (EDTA; Termo, Tokyo, Japan). The tubes were placed on ice immediately and kept there for about 15 min. After centrifugation of tubes under 4 °C at 3,000 rpm for 15 min, the plasma was immediately separated into tubes and stored at −80 °C. The tubes were kept there until (within 2 weeks to 2 months) the desired analysis for plasma amino acids. The plasma samples were deproteinized using acetonitrile at a final concentration of 80% before measurements. The amino acid concentrations in the plasma were measured by high-performance liquid chromatography–electrospray ionization–mass spectrometry (HPLC–ESI–MS) followed by precolumn derivatization which allows such measurements with high accuracy. PFAA profiling included the measurement of absolute concentrations (in μmol/L) of the following 19 amino acids: alanine (Ala), arginine (Arg), asparagine (Asn), Citrulline (Cit), glutamine (Gln), glycine (Gly), histidine (His), isoleucine (Ile), leucine (Leu), lysine (Lys), methionine (Met), ornithine (Orn), phenylalanine (Phe), proline (Pro), serine (Ser), threonine (Thr), tryptophan (Trp), tyrosine (Tyr), and valine (Val). Statistical analyses
The continuous variables in this study showed a nonnormal distribution by Kolmogorov-Smirnov and ShapiroWilk tests. Hence, statistical analyses were performed with those variables after logarithmic transformation of data.
Mahbub et al. Environmental Health and Preventive Medicine (2017) 22:7
The continuous variables were expressed as geometric mean and its 95% confidence interval (95% CI). Smoking status was categorized as current/past smokers and nonsmokers, and alcohol consumption, as current drinkers and non/rare-drinkers. For convenience of graphical presentation of individual amino acids, they were divided into 3 groups according to their plasma geometric concentrations. The differences between the patient and control groups were assessed with Student’s t-test and Chi-square (χ2) test for continuous and categorical variables, respectively. To further explore the amino acids associated with the outcome (gout), logistic regression analysis was carried out with the individual amino acids that did differ between the two groups at P < .05. The models were adjusted for the potential confounding demographic factors and corresponding odds ratios (OR), 95% confidence interval (CI) and Pvalues were obtained. All statistical tests were considered as two-tailed, and the significance level was set at the value of P < .05. The software package SPSS version 22 for
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Windows (SPSS Inc., Chicago, IL, USA) was used to perform the statistical analyses.
Results The demographic and clinical characteristics of the current study subjects have been presented in table 1. Most study participants in both patient and control groups were elderly for whom smoking and drinking were common. Statistically significant differences between the patient and control groups were observed for gender, BMI, and smoking and drinking status (P < .001 to .01). Furthermore, significant differences in terms of alanine aminotransferase/ALT, gamma-glutamyl transpeptidase/γ-GTP, high-density lipoprotein cholesterol/ HDLC, and triglycerides/TG (P < .001 to .05) were also observed between the two groups. Among the participants of this study, the commonly used medications were for hypertension (patients, 20/34 or 58.8%; controls, 329/785 or 41.9%), diabetes mellitus (patients, 5/34
Table 1 Demographic and clinical characteristics of study subjects. Values are expressed as geometric mean and 95%CI for continuous variables, and number (n) and percent (%) for categorical variables P-value*
Characteristics
Patient (n = 34)
Control (n = 785)
Geometric mean or n
95%CI or %
Geometric mean or n
95%CI or %
Age (years)
72.9
70.3–75.7
72.1
72.1–73.4
29
85.3
298
38
Gender Male Female
